Feature Extraction In Gene Expression Dataset Using Multilayer Perceptron - et. al., Nageswara Rao Eluri, (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021)
Numerous amount of gene expression datasets that are publicly available have accumulated since decades. It is hence essential to recognize and extract the instances in terms of quantitative and qualitative means.In this study, Keras is utilized to model the multilayer perceptron (MLP) to extract the features from the given input gene expression dataset. The MLP extracts the features from the test datasets after its initial training with the top extracted features from the training classifiers. Finally with the top extracted features, the MLP is fine tuned to extract optimal features from the gene expression datasets namely Gene Expression database of Normal and Tumor tissues 2 (GENT2). The experimental results shows that the proposed model achieves better feature selection than other methods in terms of accuracy, f-measure, precision and recall.